




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       argparse.ArgumentParser()
  parser.add_argument('-f'      )
  __a								=       parser.parse_args()
  return args.f





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   )					->     None:
     __a								=       logging.StreamHandler(sys.stdout   )
     logger.addHandler(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[str]:
     __a								=       get_gpu_count()

     if n_gpu > 1:
       pass
       # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
       # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
       # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
       # cmd = [sys.executable] + distributed_args + args
       # execute_subprocess_async(cmd, env=self.get_env())
       # XXX: test the results - need to save them first into .json file
     else:
       args.insert(0   ,				'run_glue_deebert.py'   )
       with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
         __a								=       run_glue_deebert.main()
         for value in result.values():
           self.assertGreaterEqual(UpperCamelCase   ,				0.666   )







   @slow
   @require_torch_non_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       '\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
     self.run_and_check(UpperCamelCase   )

     __a								=       '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
     self.run_and_check(UpperCamelCase   )

     __a								=       '\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
     self.run_and_check(UpperCamelCase   )









'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



